Open AccessDiscrete Chicken Swarm Optimization for the Quadratic Assignment Problem
Author(s) -
Soukaina Cherif Bourki Semlali,
Mohammed Essaid Riffi,
Fayçal Chebihi
Publication year - 2018
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v11.i3.pp925-935
Subject(s) - quadratic assignment problem , mathematical optimization , metaheuristic , crossover , swarm behaviour , mathematics , optimization problem , quadratic equation , benchmark (surveying) , combinatorial optimization , genetic algorithm , robustness (evolution) , local search (optimization) , assignment problem , computer science , algorithm , artificial intelligence , biochemistry , chemistry , geometry , geodesy , gene , geography
The main objective of our research is to improve an adaptation of the chicken swarm optimization algorithm (CSO) to solve the quadratic assignment problem, which is a well-known combinatorial optimization problem. The new approach is based on the CSO without using a local search, the CSO-QAP is a stochastic method inspired from the behavior of chickens in swarm while searching for food. The experiments are performed on a set of 56 benchmark QAPLIB instances. To prove the robustness of our algorithm a comparative analysis is done with the known metaheuristic of Genetic algorithm based on SCX. The average percentage of error to get the best Known solution in our proposed work with the results obtained by applying a simple genetic algorithm using sequential constructive crossover for the quadratic assignment problem. The results show the effectiveness of the proposed CSO-QAP to solve the Quadratic assignment problem in term of time and quality of solutions. The proposed adaptation can be further applied by using a local search strategy to solve the same problem or another combinatorial problem.